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Article: Landsat Snow-Free Surface Albedo Estimation over Sloping Terrain: Algorithm Development and Evaluation

TitleLandsat Snow-Free Surface Albedo Estimation over Sloping Terrain: Algorithm Development and Evaluation
Authors
KeywordsArtificial neural network (ANN)
direct estimation algorithm
discrete anisotropic radiative transfer (DART)
Landsat
sloping terrain
surface albedo
Issue Date2022
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2022, v. 60, article no. 4408914 How to Cite?
AbstractSurface albedo plays a key role in global climate modeling as a factor controlling the energy budget. Satellite observations were utilized to estimate surface albedo at global and regional scales with good precision over flat areas. However, because topography greatly complicates radiative transfer (RT) processes, estimating the albedo of rugged terrain with satellite data remains a challenge. In addition, albedo definitions over sloping terrain differ from that for flat areas. They include horizontal/horizontal sloped surface albedo (HHSA) and inclined/inclined sloped surface albedo (IISA). Methods for retrieving HHSA and IISA in mountains have not been well-explored. Here, we retrieved HHSA and IISA on sloping terrain from Landsat 8 using a direct estimation algorithm. We simulated a dataset of Landsat top-of-atmosphere (TOA) reflectance and surface albedo with discrete anisotropic radiative transfer (DART) model, for variable atmospheric, vegetation, soil, and topography properties. Then, we used artificial neural networks (ANNs) to derive an empirical relationship between TOA reflectance and surface albedo. The accuracy of our method was verified with in situ measurements: root mean squared error (RMSE) and bias equal to 0.029 and -0.010 for HHSA, and 0.023 and -0.001 for IISA, respectively. Several albedo results (HHSA, IISA, values without topographic consideration) were evaluated and compared. HHSA was found similar to albedo without topographic consideration, but IISA, considered as the 'true albedo' for sloping terrain, showed large difference from them. This study demonstrated the feasibility of surface albedo estimation from Landsat TOA reflectance directly in rugged terrains and advanced our understanding of energy budget in mountains.
Persistent Identifierhttp://hdl.handle.net/10722/323155
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMa, Yichuan-
dc.contributor.authorHe, Tao-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorWen, Jianguang-
dc.contributor.authorGastellu-Etchegorry, Jean Philippe-
dc.contributor.authorChen, Jiang-
dc.contributor.authorDIng, Anxin-
dc.contributor.authorFeng, Siqi-
dc.date.accessioned2022-11-18T11:55:06Z-
dc.date.available2022-11-18T11:55:06Z-
dc.date.issued2022-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2022, v. 60, article no. 4408914-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/323155-
dc.description.abstractSurface albedo plays a key role in global climate modeling as a factor controlling the energy budget. Satellite observations were utilized to estimate surface albedo at global and regional scales with good precision over flat areas. However, because topography greatly complicates radiative transfer (RT) processes, estimating the albedo of rugged terrain with satellite data remains a challenge. In addition, albedo definitions over sloping terrain differ from that for flat areas. They include horizontal/horizontal sloped surface albedo (HHSA) and inclined/inclined sloped surface albedo (IISA). Methods for retrieving HHSA and IISA in mountains have not been well-explored. Here, we retrieved HHSA and IISA on sloping terrain from Landsat 8 using a direct estimation algorithm. We simulated a dataset of Landsat top-of-atmosphere (TOA) reflectance and surface albedo with discrete anisotropic radiative transfer (DART) model, for variable atmospheric, vegetation, soil, and topography properties. Then, we used artificial neural networks (ANNs) to derive an empirical relationship between TOA reflectance and surface albedo. The accuracy of our method was verified with in situ measurements: root mean squared error (RMSE) and bias equal to 0.029 and -0.010 for HHSA, and 0.023 and -0.001 for IISA, respectively. Several albedo results (HHSA, IISA, values without topographic consideration) were evaluated and compared. HHSA was found similar to albedo without topographic consideration, but IISA, considered as the 'true albedo' for sloping terrain, showed large difference from them. This study demonstrated the feasibility of surface albedo estimation from Landsat TOA reflectance directly in rugged terrains and advanced our understanding of energy budget in mountains.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectArtificial neural network (ANN)-
dc.subjectdirect estimation algorithm-
dc.subjectdiscrete anisotropic radiative transfer (DART)-
dc.subjectLandsat-
dc.subjectsloping terrain-
dc.subjectsurface albedo-
dc.titleLandsat Snow-Free Surface Albedo Estimation over Sloping Terrain: Algorithm Development and Evaluation-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TGRS.2022.3149762-
dc.identifier.scopuseid_2-s2.0-85124739493-
dc.identifier.volume60-
dc.identifier.spagearticle no. 4408914-
dc.identifier.epagearticle no. 4408914-
dc.identifier.eissn1558-0644-
dc.identifier.isiWOS:000772472400025-

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